Senior Embedded Vision ML Engineer

SR2 | Socially Responsible Recruitment | Certified B Corporation™; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

Healthcare

Dental Insurance

Vision Insurance

Life Insurance

Disability Insurance

Flexible Leave

Sick Leave

Retirement Savings

FSA Options

HSA Options

Commuter Benefits

Job summary

SR2 | Socially Responsible Recruitment | Certified B Corporation™ is seeking a senior ML/CV engineer to design and optimize computer vision models for real-time embedded platforms. You will work with cross-functional teams to deploy ML into production systems and ensure robust performance on embedded hardware.

The role emphasizes strong Python/C++ coding, deep learning frameworks, and hands-on experience with model optimization and deployment on resource-constrained devices.

Qualifications

• Master's or PhD in a relevant field required.
• 5+ years of experience in machine learning and computer vision required.
• Strong programming skills in Python and C++, with experience deploying models on embedded hardware.

Responsibilities

• Design, develop, and optimize computer vision models for real-time embedded applications.
• Collaborate with multidisciplinary engineering teams to integrate ML into production systems.

Skills

Machine Learning

Computer Vision

Embedded Systems

Deep Learning

Model Optimization

Python

C++

TensorFlow

PyTorch

ONNX

TensorRT

Quantization

Pruning

Knowledge Distillation

SLAM

Object Detection

Education

Master's or PhD in a relevant field

Job description

SR2 | Socially Responsible Recruitment | Certified B Corporation™ is seeking a senior ML/CV engineer to design and optimize computer vision models for real-time embedded platforms. You will work with cross-functional teams to deploy ML into production systems and ensure robust performance on embedded hardware.

The role emphasizes strong Python/C++ coding, deep learning frameworks, and hands-on experience with model optimization and deployment on resource-constrained devices.